import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
warnings.filterwarnings('ignore')
df= pd.read_csv('audi.csv')
df.head(5)
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
df.shape
(10668, 9)
len(df)
10668
df.size
96012
df.isnull().sum()
model 0 year 0 price 0 transmission 0 mileage 0 fuelType 0 tax 0 mpg 0 engineSize 0 dtype: int64
df.dtypes
model object year int64 price int64 transmission object mileage int64 fuelType object tax int64 mpg float64 engineSize float64 dtype: object
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10668 entries, 0 to 10667 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 model 10668 non-null object 1 year 10668 non-null int64 2 price 10668 non-null int64 3 transmission 10668 non-null object 4 mileage 10668 non-null int64 5 fuelType 10668 non-null object 6 tax 10668 non-null int64 7 mpg 10668 non-null float64 8 engineSize 10668 non-null float64 dtypes: float64(2), int64(4), object(3) memory usage: 750.2+ KB
df.duplicated().sum()
103
df.loc[df.duplicated()]
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 273 | Q3 | 2019 | 34485 | Automatic | 10 | Diesel | 145 | 47.1 | 2.0 |
| 764 | Q2 | 2019 | 22495 | Manual | 1000 | Diesel | 145 | 49.6 | 1.6 |
| 784 | Q3 | 2015 | 13995 | Manual | 35446 | Diesel | 145 | 54.3 | 2.0 |
| 967 | Q5 | 2019 | 31998 | Semi-Auto | 100 | Petrol | 145 | 33.2 | 2.0 |
| 990 | Q2 | 2019 | 22495 | Manual | 1000 | Diesel | 145 | 49.6 | 1.6 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9508 | A4 | 2019 | 26990 | Automatic | 2250 | Diesel | 145 | 50.4 | 2.0 |
| 9521 | Q3 | 2019 | 26990 | Manual | 10 | Petrol | 145 | 40.9 | 1.5 |
| 9529 | Q5 | 2019 | 44990 | Automatic | 10 | Diesel | 145 | 36.2 | 2.0 |
| 9550 | Q3 | 2019 | 29995 | Manual | 10 | Petrol | 145 | 39.8 | 1.5 |
| 9597 | Q3 | 2019 | 28490 | Manual | 10 | Diesel | 145 | 42.8 | 2.0 |
103 rows × 9 columns
df.describe()
| year | price | mileage | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|
| count | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 |
| mean | 2017.100675 | 22896.685039 | 24827.244001 | 126.011436 | 50.770022 | 1.930709 |
| std | 2.167494 | 11714.841888 | 23505.257205 | 67.170294 | 12.949782 | 0.602957 |
| min | 1997.000000 | 1490.000000 | 1.000000 | 0.000000 | 18.900000 | 0.000000 |
| 25% | 2016.000000 | 15130.750000 | 5968.750000 | 125.000000 | 40.900000 | 1.500000 |
| 50% | 2017.000000 | 20200.000000 | 19000.000000 | 145.000000 | 49.600000 | 2.000000 |
| 75% | 2019.000000 | 27990.000000 | 36464.500000 | 145.000000 | 58.900000 | 2.000000 |
| max | 2020.000000 | 145000.000000 | 323000.000000 | 580.000000 | 188.300000 | 6.300000 |
df.describe(include='object')
| model | transmission | fuelType | |
|---|---|---|---|
| count | 10668 | 10668 | 10668 |
| unique | 26 | 3 | 3 |
| top | A3 | Manual | Diesel |
| freq | 1929 | 4369 | 5577 |
from ydata_profiling import ProfileReport
ProfileReport(df, title='Profiling Report')
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df['model'].unique()
array([' A1', ' A6', ' A4', ' A3', ' Q3', ' Q5', ' A5', ' S4', ' Q2',
' A7', ' TT', ' Q7', ' RS6', ' RS3', ' A8', ' Q8', ' RS4', ' RS5',
' R8', ' SQ5', ' S8', ' SQ7', ' S3', ' S5', ' A2', ' RS7'],
dtype=object)
pd.DataFrame(df['model'].value_counts()).reset_index()
| index | model | |
|---|---|---|
| 0 | A3 | 1929 |
| 1 | Q3 | 1417 |
| 2 | A4 | 1381 |
| 3 | A1 | 1347 |
| 4 | A5 | 882 |
| 5 | Q5 | 877 |
| 6 | Q2 | 822 |
| 7 | A6 | 748 |
| 8 | Q7 | 397 |
| 9 | TT | 336 |
| 10 | A7 | 122 |
| 11 | A8 | 118 |
| 12 | Q8 | 69 |
| 13 | RS6 | 39 |
| 14 | RS3 | 33 |
| 15 | RS4 | 31 |
| 16 | RS5 | 29 |
| 17 | R8 | 28 |
| 18 | S3 | 18 |
| 19 | SQ5 | 16 |
| 20 | S4 | 12 |
| 21 | SQ7 | 8 |
| 22 | S8 | 4 |
| 23 | S5 | 3 |
| 24 | A2 | 1 |
| 25 | RS7 | 1 |
df['transmission'].unique()
array(['Manual', 'Automatic', 'Semi-Auto'], dtype=object)
df['transmission'].value_counts().to_frame()
| transmission | |
|---|---|
| Manual | 4369 |
| Semi-Auto | 3591 |
| Automatic | 2708 |
df['fuelType'].value_counts().to_frame()
| fuelType | |
|---|---|
| Diesel | 5577 |
| Petrol | 5063 |
| Hybrid | 28 |
engine = df['engineSize'].value_counts().to_frame().reset_index()
engine.rename(columns={'index':'Engine_size','engineSize':'Counts'})
| Engine_size | Counts | |
|---|---|---|
| 0 | 2.0 | 5169 |
| 1 | 1.4 | 1594 |
| 2 | 3.0 | 1149 |
| 3 | 1.6 | 913 |
| 4 | 1.5 | 744 |
| 5 | 1.0 | 558 |
| 6 | 4.0 | 154 |
| 7 | 1.8 | 126 |
| 8 | 2.5 | 61 |
| 9 | 0.0 | 57 |
| 10 | 2.9 | 49 |
| 11 | 1.2 | 31 |
| 12 | 4.2 | 25 |
| 13 | 5.2 | 23 |
| 14 | 3.2 | 5 |
| 15 | 1.9 | 4 |
| 16 | 2.7 | 3 |
| 17 | 4.1 | 2 |
| 18 | 6.3 | 1 |
df.corr('pearson')
| year | price | mileage | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|
| year | 1.000000 | 0.592581 | -0.789667 | 0.093066 | -0.351281 | -0.031582 |
| price | 0.592581 | 1.000000 | -0.535357 | 0.356157 | -0.600334 | 0.591262 |
| mileage | -0.789667 | -0.535357 | 1.000000 | -0.166547 | 0.395103 | 0.070710 |
| tax | 0.093066 | 0.356157 | -0.166547 | 1.000000 | -0.635909 | 0.393075 |
| mpg | -0.351281 | -0.600334 | 0.395103 | -0.635909 | 1.000000 | -0.365621 |
| engineSize | -0.031582 | 0.591262 | 0.070710 | 0.393075 | -0.365621 | 1.000000 |
df.corr('spearman')
| year | price | mileage | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|
| year | 1.000000 | 0.765990 | -0.862416 | 0.194361 | -0.532725 | 0.026446 |
| price | 0.765990 | 1.000000 | -0.709120 | 0.422473 | -0.749557 | 0.465471 |
| mileage | -0.862416 | -0.709120 | 1.000000 | -0.180303 | 0.530829 | 0.034433 |
| tax | 0.194361 | 0.422473 | -0.180303 | 1.000000 | -0.601924 | 0.303448 |
| mpg | -0.532725 | -0.749557 | 0.530829 | -0.601924 | 1.000000 | -0.368087 |
| engineSize | 0.026446 | 0.465471 | 0.034433 | 0.303448 | -0.368087 | 1.000000 |
df.head()
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
df['year'].unique()
array([2017, 2016, 2019, 2015, 2014, 2018, 2013, 2020, 2004, 2009, 2012,
2010, 2007, 2011, 2008, 2003, 2005, 2002, 2006, 1998, 1997],
dtype=int64)
sns.scatterplot(data=df, x='year',y='price')
plt.show()
plt.figure(figsize=(12,4))
sns.heatmap(df.corr(),annot=True)
plt.show()
plt.figure(figsize=(12,4))
sns.barplot(data=df, x='year', y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.show()
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
sns.lineplot(data=df, x='year',y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.subplot(1,3,2)
sns.barplot(data=df, x='model',y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.subplot(1,3,3)
sns.barplot(data=df, x='transmission',y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.show()
plt.figure(figsize=(12,5))
sns.barplot(data=df, x='fuelType', y='price',hue='transmission')
ax=plt.gca()
ax.set_facecolor('black')
plt.show()
sns.boxplot(data=df, x='transmission',y='price')
plt.show()
plt.figure(figsize=(12,4))
sns.boxplot(df['price'])
plt.show()
display(df[['transmission','price']].describe())
| price | |
|---|---|
| count | 10668.000000 |
| mean | 22896.685039 |
| std | 11714.841888 |
| min | 1490.000000 |
| 25% | 15130.750000 |
| 50% | 20200.000000 |
| 75% | 27990.000000 |
| max | 145000.000000 |
q1 = df['price'].quantile(0.25)
q3 = df['price'].quantile(0.75)
q1,q3
(15130.75, 27990.0)
IQR = q3-q1
IQR
12859.25
upper_whisker = q3+1.5*IQR
upper_whisker
47278.875
df[df['price'] > 47278.875]
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 198 | Q7 | 2020 | 62985 | Semi-Auto | 10 | Diesel | 145 | 33.2 | 3.0 |
| 221 | Q7 | 2019 | 49985 | Automatic | 10 | Diesel | 145 | 33.2 | 3.0 |
| 222 | Q7 | 2019 | 59995 | Automatic | 10 | Diesel | 145 | 33.2 | 3.0 |
| 223 | Q5 | 2020 | 47895 | Semi-Auto | 10 | Petrol | 145 | 30.7 | 2.0 |
| 247 | Q7 | 2019 | 56985 | Automatic | 1510 | Diesel | 145 | 33.2 | 3.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10530 | RS6 | 2016 | 52950 | Automatic | 27000 | Petrol | 325 | 29.4 | 4.0 |
| 10546 | A4 | 2020 | 49000 | Automatic | 38 | Diesel | 145 | 39.2 | 3.0 |
| 10597 | Q7 | 2020 | 68000 | Automatic | 4162 | Diesel | 145 | 32.1 | 3.0 |
| 10602 | Q7 | 2020 | 53792 | Automatic | 3182 | Diesel | 145 | 33.2 | 3.0 |
| 10648 | RS6 | 2016 | 49990 | Automatic | 24000 | Petrol | 325 | 29.4 | 4.0 |
443 rows × 9 columns
lower_whisker = q1-1.5*IQR
lower_whisker
-4158.125
df[df['price'] < -4158.125]
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize |
|---|
df.groupby('transmission')['price'].count()
transmission Automatic 2708 Manual 4369 Semi-Auto 3591 Name: price, dtype: int64
mannual = df[df['transmission']=='Manual']['price'].to_frame()
mannual
| price | |
|---|---|
| 0 | 12500 |
| 2 | 11000 |
| 4 | 17300 |
| 7 | 11750 |
| 8 | 10200 |
| ... | ... |
| 10662 | 12695 |
| 10663 | 16999 |
| 10664 | 16999 |
| 10665 | 17199 |
| 10667 | 15999 |
4369 rows × 1 columns
mannual.describe()
| price | |
|---|---|
| count | 4369.000000 |
| mean | 16101.033417 |
| std | 5519.089663 |
| min | 1699.000000 |
| 25% | 11995.000000 |
| 50% | 15700.000000 |
| 75% | 19325.000000 |
| max | 47995.000000 |
m1 = mannual['price'].quantile(0.25)
m3 = mannual['price'].quantile(0.75)
MIQR = m3-m1
mupper_whisker = m3+1.5*MIQR
mupper_whisker
30320.0
mannual[mannual['price'] > 30320.0].T
| 264 | 320 | 363 | 514 | 515 | 625 | 713 | 854 | 1175 | 1869 | ... | 7668 | 7694 | 8674 | 9334 | 9437 | 9468 | 9498 | 9563 | 9564 | 9618 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| price | 31985 | 31995 | 31985 | 34259 | 35131 | 35995 | 37995 | 30995 | 30491 | 47995 | ... | 30990 | 30990 | 31495 | 32990 | 32490 | 38990 | 33490 | 30990 | 30990 | 33990 |
1 rows × 46 columns
automatic = df[df['transmission']=='Automatic']['price'].to_frame()
automatic.T
| 1 | 3 | 5 | 6 | 11 | 15 | 16 | 17 | 18 | 20 | ... | 10640 | 10641 | 10642 | 10643 | 10644 | 10647 | 10648 | 10655 | 10660 | 10666 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| price | 16500 | 16800 | 13900 | 13250 | 16500 | 14500 | 15700 | 13900 | 19000 | 17300 | ... | 37000 | 25000 | 33000 | 30000 | 25000 | 21000 | 49990 | 29995 | 9995 | 19499 |
1 rows × 2708 columns
a1 = automatic['price'].quantile(0.25)
a3 = automatic['price'].quantile(0.75)
AIQR = a3-a1
aupper_whisker = a3+1.5*AIQR
aupper_whisker
54801.5
automatic[automatic['price'] > 54801.5].T
| 222 | 247 | 640 | 732 | 1077 | 1107 | 1163 | 1196 | 1545 | 1838 | ... | 10072 | 10085 | 10098 | 10225 | 10339 | 10468 | 10470 | 10486 | 10521 | 10597 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| price | 59995 | 56985 | 60995 | 55500 | 56996 | 84496 | 63796 | 59996 | 56490 | 77888 | ... | 74500 | 57895 | 69890 | 104948 | 72500 | 125000 | 70000 | 59500 | 58000 | 68000 |
1 rows × 101 columns
semi_auto = df[df['transmission']=='Semi-Auto']['price'].to_frame()
semi_auto.T
| 90 | 91 | 99 | 101 | 105 | 106 | 168 | 170 | 171 | 174 | ... | 10512 | 10514 | 10533 | 10581 | 10582 | 10586 | 10654 | 10656 | 10657 | 10659 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| price | 25499 | 25499 | 25499 | 31000 | 26000 | 30000 | 11498 | 18998 | 25498 | 19998 | ... | 16999 | 27999 | 18950 | 23000 | 16495 | 31000 | 16495 | 15495 | 20995 | 27995 |
1 rows × 3591 columns
sa1 = semi_auto['price'].quantile(0.25)
sa3 = semi_auto['price'].quantile(0.75)
SAIQR = sa3-sa1
saupper_whisker = sa3+1.5*SAIQR
saupper_whisker
50268.0
automatic[automatic['price'] > 50268.0].T
| 222 | 247 | 293 | 640 | 732 | 1077 | 1107 | 1163 | 1187 | 1196 | ... | 10339 | 10468 | 10470 | 10486 | 10496 | 10521 | 10526 | 10530 | 10597 | 10602 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| price | 59995 | 56985 | 52985 | 60995 | 55500 | 56996 | 84496 | 63796 | 52496 | 59996 | ... | 72500 | 125000 | 70000 | 59500 | 51495 | 58000 | 51000 | 52950 | 68000 | 53792 |
1 rows × 155 columns
sns.pairplot(df)
plt.show()
df.sample(10)
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 5208 | A4 | 2018 | 21450 | Semi-Auto | 9555 | Petrol | 145 | 50.4 | 1.4 |
| 8535 | A1 | 2016 | 8500 | Manual | 75869 | Diesel | 0 | 80.7 | 1.6 |
| 5935 | A3 | 2019 | 23900 | Manual | 1529 | Diesel | 150 | 52.3 | 1.6 |
| 10559 | Q2 | 2019 | 24500 | Automatic | 8334 | Petrol | 145 | 40.9 | 1.5 |
| 3551 | A3 | 2013 | 11980 | Semi-Auto | 59386 | Petrol | 125 | 50.4 | 1.8 |
| 6730 | A3 | 2017 | 23475 | Semi-Auto | 27524 | Petrol | 160 | 43.5 | 2.0 |
| 2926 | A4 | 2018 | 23132 | Semi-Auto | 15475 | Petrol | 145 | 50.4 | 2.0 |
| 4888 | A3 | 2017 | 13500 | Manual | 51578 | Petrol | 20 | 60.1 | 1.4 |
| 1800 | A4 | 2017 | 16995 | Automatic | 21641 | Petrol | 145 | 51.4 | 1.4 |
| 2887 | A3 | 2018 | 20971 | Semi-Auto | 22604 | Petrol | 145 | 56.5 | 1.5 |
x = df.iloc[:,[0,1,3,4,5,6,7,8]].values
x
array([[' A1', 2017, 'Manual', ..., 150, 55.4, 1.4],
[' A6', 2016, 'Automatic', ..., 20, 64.2, 2.0],
[' A1', 2016, 'Manual', ..., 30, 55.4, 1.4],
...,
[' A3', 2020, 'Manual', ..., 150, 49.6, 1.0],
[' Q3', 2017, 'Automatic', ..., 150, 47.9, 1.4],
[' Q3', 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)
x.shape
(10668, 8)
y = df.iloc[:,[2]].values
y
array([[12500],
[16500],
[11000],
...,
[17199],
[19499],
[15999]], dtype=int64)
y.shape
(10668, 1)
pd.DataFrame(x).head(5)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
pd.DataFrame(y).head(5)
| 0 | |
|---|---|
| 0 | 12500 |
| 1 | 16500 |
| 2 | 11000 |
| 3 | 16800 |
| 4 | 17300 |
from sklearn.preprocessing import LabelEncoder
le1 = LabelEncoder()
le1
LabelEncoder()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LabelEncoder()
x[:,0]
array([' A1', ' A6', ' A1', ..., ' A3', ' Q3', ' Q3'], dtype=object)
x[:,0] = le1.fit_transform(x[:,0])
x[:,0]
array([0, 5, 0, ..., 2, 9, 9], dtype=object)
le2 = LabelEncoder()
le2
LabelEncoder()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LabelEncoder()
x[:,4]
array(['Petrol', 'Diesel', 'Petrol', ..., 'Petrol', 'Petrol', 'Petrol'],
dtype=object)
x[:,4]=le2.fit_transform(x[:,4])
x[:,4]
array([2, 0, 2, ..., 2, 2, 2], dtype=object)
pd.DataFrame(x).head(5)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 2017 | Manual | 15735 | 2 | 150 | 55.4 | 1.4 |
| 1 | 5 | 2016 | Automatic | 36203 | 0 | 20 | 64.2 | 2.0 |
| 2 | 0 | 2016 | Manual | 29946 | 2 | 30 | 55.4 | 1.4 |
| 3 | 3 | 2017 | Automatic | 25952 | 0 | 145 | 67.3 | 2.0 |
| 4 | 2 | 2019 | Manual | 1998 | 2 | 145 | 49.6 | 1.0 |
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[2])],remainder='passthrough')
ct
ColumnTransformer(remainder='passthrough',
transformers=[('encoder', OneHotEncoder(), [2])])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. ColumnTransformer(remainder='passthrough',
transformers=[('encoder', OneHotEncoder(), [2])])[2]
OneHotEncoder()
passthrough
x = ct.fit_transform(x)
x
array([[0.0, 1.0, 0.0, ..., 150, 55.4, 1.4],
[1.0, 0.0, 0.0, ..., 20, 64.2, 2.0],
[0.0, 1.0, 0.0, ..., 30, 55.4, 1.4],
...,
[0.0, 1.0, 0.0, ..., 150, 49.6, 1.0],
[1.0, 0.0, 0.0, ..., 150, 47.9, 1.4],
[0.0, 1.0, 0.0, ..., 150, 47.9, 1.4]], dtype=object)
x.shape
(10668, 10)
pd.DataFrame(x)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 1.0 | 0.0 | 0 | 2017 | 15735 | 2 | 150 | 55.4 | 1.4 |
| 1 | 1.0 | 0.0 | 0.0 | 5 | 2016 | 36203 | 0 | 20 | 64.2 | 2.0 |
| 2 | 0.0 | 1.0 | 0.0 | 0 | 2016 | 29946 | 2 | 30 | 55.4 | 1.4 |
| 3 | 1.0 | 0.0 | 0.0 | 3 | 2017 | 25952 | 0 | 145 | 67.3 | 2.0 |
| 4 | 0.0 | 1.0 | 0.0 | 2 | 2019 | 1998 | 2 | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 4018 | 2 | 145 | 49.6 | 1.0 |
| 10664 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 1978 | 2 | 150 | 49.6 | 1.0 |
| 10665 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 609 | 2 | 150 | 49.6 | 1.0 |
| 10666 | 1.0 | 0.0 | 0.0 | 9 | 2017 | 8646 | 2 | 150 | 47.9 | 1.4 |
| 10667 | 0.0 | 1.0 | 0.0 | 9 | 2016 | 11855 | 2 | 150 | 47.9 | 1.4 |
10668 rows × 10 columns
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc
StandardScaler()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
StandardScaler()
x = sc.fit_transform(x)
x
array([[-0.58326752, 1.2007284 , -0.71233307, ..., 0.35714729,
0.35755001, -0.88021837],
[ 1.71447913, -0.83282781, -0.71233307, ..., -1.57832278,
1.03713001, 0.11492465],
[-0.58326752, 1.2007284 , -0.71233307, ..., -1.42944047,
0.35755001, -0.88021837],
...,
[-0.58326752, 1.2007284 , -0.71233307, ..., 0.35714729,
-0.09035499, -1.54364705],
[ 1.71447913, -0.83282781, -0.71233307, ..., 0.35714729,
-0.22163749, -0.88021837],
[-0.58326752, 1.2007284 , -0.71233307, ..., 0.35714729,
-0.22163749, -0.88021837]])
pd.DataFrame(x)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.583268 | 1.200728 | -0.712333 | -1.123544 | -0.046450 | -0.386836 | 1.050783 | 0.357147 | 0.357550 | -0.880218 |
| 1 | 1.714479 | -0.832828 | -0.712333 | -0.160831 | -0.507834 | 0.483989 | -0.954181 | -1.578323 | 1.037130 | 0.114925 |
| 2 | -0.583268 | 1.200728 | -0.712333 | -1.123544 | -0.507834 | 0.217781 | 1.050783 | -1.429440 | 0.357550 | -0.880218 |
| 3 | 1.714479 | -0.832828 | -0.712333 | -0.545916 | -0.046450 | 0.047853 | -0.954181 | 0.282706 | 1.276528 | 0.114925 |
| 4 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 0.876318 | -0.971285 | 1.050783 | 0.282706 | -0.090355 | -1.543647 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 1.337702 | -0.885343 | 1.050783 | 0.282706 | -0.090355 | -1.543647 |
| 10664 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 1.337702 | -0.972136 | 1.050783 | 0.357147 | -0.090355 | -1.543647 |
| 10665 | -0.583268 | 1.200728 | -0.712333 | -0.738459 | 1.337702 | -1.030381 | 1.050783 | 0.357147 | -0.090355 | -1.543647 |
| 10666 | 1.714479 | -0.832828 | -0.712333 | 0.609339 | -0.046450 | -0.688442 | 1.050783 | 0.357147 | -0.221637 | -0.880218 |
| 10667 | -0.583268 | 1.200728 | -0.712333 | 0.609339 | -0.507834 | -0.551913 | 1.050783 | 0.357147 | -0.221637 | -0.880218 |
10668 rows × 10 columns
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x,y, random_state=0, test_size=0.2)
x.shape
(10668, 10)
y.shape
(10668, 1)
X_train.shape, y_train.shape
((8534, 10), (8534, 1))
X_test.shape, y_test.shape
((2134, 10), (2134, 1))
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(random_state=0)
reg
RandomForestRegressor(random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(random_state=0)
reg.fit(X_train, y_train)
RandomForestRegressor(random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(random_state=0)
display(reg)
RandomForestRegressor(random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(random_state=0)
y_pred = reg.predict(X_test)
y_pred
array([14337.15, 23450.35, 27330.07, ..., 46275.18, 31359. , 9929.62])
len(y_pred)
2134
len(y_test)
2134
y_pred.reshape(len(y_pred),1)
array([[14337.15],
[23450.35],
[27330.07],
...,
[46275.18],
[31359. ],
[ 9929.62]])
y_test.reshape(len(y_test),1)
array([[14998],
[21950],
[28990],
...,
[45995],
[30500],
[ 8400]], dtype=int64)
np.concatenate((y_pred.reshape(len(y_pred),1),y_test.reshape(len(y_test),1)),1)
array([[14337.15, 14998. ],
[23450.35, 21950. ],
[27330.07, 28990. ],
...,
[46275.18, 45995. ],
[31359. , 30500. ],
[ 9929.62, 8400. ]])
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error
print("The Accuracy for the model as r2_score will have :",r2_score(y_test,y_pred))
print("+"*70)
The Accuracy for the model as r2_score will have : 0.9536134841307546 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
print("The mean absolute error for the model we have",mean_absolute_error(y_test,y_pred))
print("+"*70)
The mean absolute error for the model we have 1538.730980670462 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
from sklearn.linear_model import LinearRegression
ler = LinearRegression()
ler
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
ler.fit(X_train, y_train)
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
ler
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
y_predler = ler.predict(X_test)
y_predler
array([[13053.11094043],
[29339.11094043],
[31899.11094043],
...,
[42661.11094043],
[31567.11094043],
[ 7286.11094043]])
y_test
array([[14998],
[21950],
[28990],
...,
[45995],
[30500],
[ 8400]], dtype=int64)
np.concatenate((y_predler.reshape(len(y_predler),1), y_test.reshape(len(y_test),1)),1)
array([[13053.11094043, 14998. ],
[29339.11094043, 21950. ],
[31899.11094043, 28990. ],
...,
[42661.11094043, 45995. ],
[31567.11094043, 30500. ],
[ 7286.11094043, 8400. ]])
from sklearn.metrics import r2_score, mean_absolute_error
print("The Accuracy for model Linear will be :",r2_score(y_test, y_predler))
print("+"*55)
The Accuracy for model Linear will be : 0.79159249215952 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
print("The mean sqaure error for the Linear model will be: ",mean_absolute_error(y_test, y_predler))
print("+"*70)
The mean sqaure error for the Linear model will be: 3381.661027107596 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
y_predlecom = ler.predict(x)
y_predlecom
array([[14619.11094043],
[20627.11094043],
[13806.11094043],
...,
[19393.11094043],
[20990.11094043],
[16681.11094043]])
result = pd.concat([df,pd.DataFrame(y_predlecom)],axis=1)
result = result.rename(columns={0:"Predicted_Price"})
result['Difference'] = result['Predicted_Price'] - result['price']
result.sample(10)
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | Predicted_Price | Difference | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 4755 | Q2 | 2016 | 19990 | Semi-Auto | 25000 | Petrol | 125 | 52.3 | 1.4 | 16485.11094 | -3504.88906 |
| 3771 | A3 | 2019 | 21000 | Manual | 4500 | Diesel | 145 | 51.4 | 1.6 | 22060.11094 | 1060.11094 |
| 4489 | Q3 | 2019 | 34990 | Automatic | 7199 | Petrol | 145 | 32.1 | 2.0 | 34207.11094 | -782.88906 |
| 8022 | Q3 | 2017 | 17499 | Automatic | 34692 | Petrol | 145 | 47.9 | 1.4 | 18623.11094 | 1124.11094 |
| 6752 | Q2 | 2020 | 25888 | Semi-Auto | 4000 | Petrol | 145 | 42.2 | 1.5 | 28861.11094 | 2973.11094 |
| 7387 | Q2 | 2017 | 20490 | Manual | 10700 | Petrol | 150 | 50.4 | 1.4 | 18140.11094 | -2349.88906 |
| 7240 | A5 | 2019 | 25990 | Automatic | 10664 | Diesel | 145 | 50.4 | 2.0 | 28413.11094 | 2423.11094 |
| 9992 | Q3 | 2020 | 34000 | Automatic | 1000 | Petrol | 145 | 31.4 | 2.0 | 36939.11094 | 2939.11094 |
| 8985 | A4 | 2017 | 19599 | Automatic | 28367 | Petrol | 145 | 49.6 | 1.4 | 17269.11094 | -2329.88906 |
| 1877 | A5 | 2015 | 15750 | Automatic | 59886 | Petrol | 160 | 44.1 | 1.8 | 15302.11094 | -447.88906 |
from sklearn.ensemble import ExtraTreesRegressor
ET_Model = ExtraTreesRegressor(n_estimators=120)
ET_Model
ExtraTreesRegressor(n_estimators=120)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
ExtraTreesRegressor(n_estimators=120)
ET_Model.fit(X_train, y_train)
ExtraTreesRegressor(n_estimators=120)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
ExtraTreesRegressor(n_estimators=120)
y_predetr = ET_Model.predict(X_test)
y_predetr
array([14017.575 , 22288.375 , 28787.33333333, ...,
49635.35833333, 30444.25 , 9988.24166667])
y_test
array([[14998],
[21950],
[28990],
...,
[45995],
[30500],
[ 8400]], dtype=int64)
print("The Accuracy for the Extra Tree Regressor is :",r2_score(y_test, y_predetr))
print("+"*65)
The Accuracy for the Extra Tree Regressor is : 0.9566191355841895 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
print("The mean absolute error for the Extra Tree Regressor is :",mean_absolute_error(y_test, y_predetr))
print("+"*75)
The mean absolute error for the Extra Tree Regressor is : 1539.3485770071852 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
y_predetcom = ET_Model.predict(x)
y_predetcom
array([12500. , 16500. , 11000. , ...,
17199. , 19499. , 18056.00833333])
resultet = pd.concat([df,pd.DataFrame(y_predetcom)],axis=1)
resultet = resultet.rename(columns={0:"Predicted_value"})
resultet['Difference'] = resultet['Predicted_value'] - resultet['price']
resultet.sample(20)
| model | year | price | transmission | mileage | fuelType | tax | mpg | engineSize | Predicted_value | Difference | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 998 | Q5 | 2017 | 33495 | Semi-Auto | 37492 | Petrol | 150 | 34.0 | 3.0 | 33495.000000 | 0.000000 |
| 8022 | Q3 | 2017 | 17499 | Automatic | 34692 | Petrol | 145 | 47.9 | 1.4 | 18031.516667 | 532.516667 |
| 4212 | A1 | 2014 | 10398 | Manual | 24856 | Petrol | 125 | 52.3 | 1.4 | 10398.000000 | 0.000000 |
| 9035 | A1 | 2015 | 12681 | Manual | 10747 | Petrol | 30 | 55.4 | 1.4 | 12681.000000 | 0.000000 |
| 1047 | A6 | 2019 | 29995 | Automatic | 4985 | Diesel | 145 | 50.4 | 2.0 | 29995.000000 | 0.000000 |
| 691 | A4 | 2017 | 17298 | Manual | 6402 | Petrol | 145 | 52.3 | 1.4 | 17298.000000 | 0.000000 |
| 7537 | A4 | 2016 | 16500 | Automatic | 40882 | Diesel | 20 | 67.3 | 2.0 | 16045.166667 | -454.833333 |
| 3485 | Q5 | 2019 | 36991 | Automatic | 10 | Diesel | 145 | 38.2 | 2.0 | 37889.000000 | 898.000000 |
| 4501 | A5 | 2019 | 31990 | Manual | 6000 | Petrol | 145 | 37.2 | 2.0 | 31990.000000 | 0.000000 |
| 2022 | Q2 | 2019 | 21990 | Semi-Auto | 5445 | Diesel | 150 | 47.9 | 1.6 | 21990.000000 | 0.000000 |
| 353 | A4 | 2020 | 40780 | Semi-Auto | 6746 | Diesel | 145 | 45.6 | 2.0 | 40780.000000 | 0.000000 |
| 1778 | A4 | 2016 | 16750 | Semi-Auto | 50715 | Diesel | 30 | 65.7 | 2.0 | 16750.000000 | 0.000000 |
| 4834 | A3 | 2019 | 21990 | Manual | 4875 | Petrol | 145 | 42.2 | 1.5 | 23310.233333 | 1320.233333 |
| 7274 | A4 | 2016 | 16990 | Manual | 29588 | Diesel | 20 | 70.6 | 2.0 | 16990.000000 | 0.000000 |
| 1044 | Q5 | 2017 | 32995 | Semi-Auto | 32821 | Diesel | 235 | 42.2 | 3.0 | 32995.000000 | 0.000000 |
| 6926 | A8 | 2018 | 34399 | Automatic | 11300 | Diesel | 145 | 50.4 | 3.0 | 34399.000000 | 0.000000 |
| 1253 | A3 | 2014 | 11995 | Manual | 24175 | Petrol | 20 | 60.1 | 1.4 | 11995.000000 | 0.000000 |
| 2521 | A4 | 2018 | 18995 | Manual | 14912 | Petrol | 145 | 51.4 | 1.4 | 17869.333333 | -1125.666667 |
| 221 | Q7 | 2019 | 49985 | Automatic | 10 | Diesel | 145 | 33.2 | 3.0 | 59995.000000 | 10010.000000 |
| 6614 | A4 | 2017 | 16000 | Semi-Auto | 26048 | Diesel | 145 | 72.4 | 2.0 | 16000.000000 | 0.000000 |
from sklearn.model_selection import RandomizedSearchCV
n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]
n_estimators
[80, 237, 395, 553, 711, 868, 1026, 1184, 1342, 1500]
max_features = ['auto', 'sqrt']
max_features
['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]
max_depth
[6, 15, 25, 35, 45]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_split
[2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10]
min_samples_leaf
[1, 2, 5, 10]
rand_grid={'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf}
rand_grid
{'n_estimators': [80, 237, 395, 553, 711, 868, 1026, 1184, 1342, 1500],
'max_features': ['auto', 'sqrt'],
'max_depth': [6, 15, 25, 35, 45],
'min_samples_split': [2, 5, 10, 15, 100],
'min_samples_leaf': [1, 2, 5, 10]}
rf = RandomForestRegressor()
rf
RandomForestRegressor()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor()
rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=3,cv=3,random_state=42, n_jobs = 1)
rCV
RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
param_distributions={'max_depth': [6, 15, 25, 35, 45],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 5, 10],
'min_samples_split': [2, 5, 10, 15,
100],
'n_estimators': [80, 237, 395, 553, 711,
868, 1026, 1184, 1342,
1500]},
random_state=42, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
param_distributions={'max_depth': [6, 15, 25, 35, 45],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 5, 10],
'min_samples_split': [2, 5, 10, 15,
100],
'n_estimators': [80, 237, 395, 553, 711,
868, 1026, 1184, 1342,
1500]},
random_state=42, scoring='neg_mean_squared_error')RandomForestRegressor()
RandomForestRegressor()
rCV.fit(X_train,y_train)
RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
param_distributions={'max_depth': [6, 15, 25, 35, 45],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 5, 10],
'min_samples_split': [2, 5, 10, 15,
100],
'n_estimators': [80, 237, 395, 553, 711,
868, 1026, 1184, 1342,
1500]},
random_state=42, scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
param_distributions={'max_depth': [6, 15, 25, 35, 45],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 5, 10],
'min_samples_split': [2, 5, 10, 15,
100],
'n_estimators': [80, 237, 395, 553, 711,
868, 1026, 1184, 1342,
1500]},
random_state=42, scoring='neg_mean_squared_error')RandomForestRegressor()
RandomForestRegressor()
rf_pred = rCV.predict(X_test)
rf_pred
array([14099.8108772 , 23761.77940499, 28500.80689889, ...,
47952.547461 , 31290.62208548, 10063.56027917])
from sklearn.metrics import mean_absolute_error, mean_squared_error
print("Mean aboslute error for model :",mean_absolute_error(y_test,rf_pred))
print("+"*50)
Mean aboslute error for model : 1501.6334599901706 ++++++++++++++++++++++++++++++++++++++++++++++++++
print("Mean Sqaured error for model :",mean_squared_error(y_test, rf_pred))
print("+"*50)
Mean Sqaured error for model : 5704930.425204219 ++++++++++++++++++++++++++++++++++++++++++++++++++
print("The Accuracy for the Model will be :",r2_score(y_test, rf_pred))
print("+"*50)
The Accuracy for the Model will be : 0.958484238238705 ++++++++++++++++++++++++++++++++++++++++++++++++++
from catboost import CatBoostRegressor
cat = CatBoostRegressor()
cat
<catboost.core.CatBoostRegressor at 0x2ae3d268fa0>
cat.fit(X_train, y_train)
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<catboost.core.CatBoostRegressor at 0x2ae3d268fa0>
cat_pred = cat.predict(X_test)
cat_pred
array([13386.63817795, 24056.86842882, 28082.99514751, ...,
45959.31234642, 31714.44751009, 9481.45994163])
print("The Accuracy for the Catboost model analysing are :",r2_score(y_test, cat_pred))
print("+"*70)
The Accuracy for the Catboost model analysing are : 0.9641612028134969 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++